Operation Of Power Transformers Research Articles

Evaluating the insulation condition of oil‐impregnated paper bushings based on the time constant database

AbstractAssessing the insulation condition of oil‐impregnated paper (OIP) bushings is necessary to guarantee the safe operation of power transformers. Moisture is the main threat to the insulation of OIP bushings. Therefore, accurately estimating their moisture value is crucial to keep power systems running smoothly. However, it has been suggested that the ageing status of OIP bushings should be considered while assessing the moisture. Dielectric frequency‐domain spectroscopy (FDS) has been extensively employed for assessing insulation condition of OIP insulations due to its benefits. A new approach was proposed to assess the insulation condition of OIP bushings using FDS results. Various OIP samples with various moisture levels and degrees of polymerisation (DP) were selected. The extended Debye model (EDM) parameters related to each sample were calculated using their tanδ − f curve. A time constant database (TCD) was created using the obtained time constants of EDM, which includes the dominant time constant and time constant related to the R0 – C0 branch, and their corresponding insulation condition. Then, a method was introduced based on the created TCD to estimate the insulation condition of OIP bushings. The results showed that the average relative error of moisture and DP estimation is 8.33% and 6.32%, respectively.

Evaluating the insulation condition of oil‐impregnated paper bushings based on the time constant database

AbstractAssessing the insulation condition of oil‐impregnated paper (OIP) bushings is necessary to guarantee the safe operation of power transformers. Moisture is the main threat to the insulation of OIP bushings. Therefore, accurately estimating their moisture value is crucial to keep power systems running smoothly. However, it has been suggested that the ageing status of OIP bushings should be considered while assessing the moisture. Dielectric frequency‐domain spectroscopy (FDS) has been extensively employed for assessing insulation condition of OIP insulations due to its benefits. A new approach was proposed to assess the insulation condition of OIP bushings using FDS results. Various OIP samples with various moisture levels and degrees of polymerisation (DP) were selected. The extended Debye model (EDM) parameters related to each sample were calculated using their tanδ − f curve. A time constant database (TCD) was created using the obtained time constants of EDM, which includes the dominant time constant and time constant related to the R0 – C0 branch, and their corresponding insulation condition. Then, a method was introduced based on the created TCD to estimate the insulation condition of OIP bushings. The results showed that the average relative error of moisture and DP estimation is 8.33% and 6.32%, respectively.

Partial Discharge Fault Diagnosis in Power Transformers Based on SGMD Approximate Entropy and Optimized BILSTM

Partial discharge (PD) fault diagnosis is of great importance for ensuring the safe and stable operation of power transformers. To address the issues of low accuracy in traditional PD fault diagnostic methods, this paper proposes a novel method for the power transformer PD fault diagnosis. It incorporates the approximate entropy (ApEn) of symplectic geometry mode decomposition (SGMD) into the optimized bidirectional long short-term memory (BILSTM) neural network. This method extracts dominant PD features employing SGMD and ApEn. Meanwhile, it improves the diagnostic accuracy with the optimized BILSTM by introducing the golden jackal optimization (GJO). Simulation studies evaluate the performance of FFT, EMD, VMD, and SGMD. The results show that SGMD–ApEn outperforms other methods in extracting dominant PD features. Experimental results verify the effectiveness and superiority of the proposed method by comparing different traditional methods. The proposed method improves PD fault recognition accuracy and provides a diagnostic rate of 98.6%, with lower noise sensitivity.

Research on infrared spectroscopy detection of furfural content in transformer oil based on acetonitrile extraction

Abstract The stable operation of power transformers depends on the oil-paper insulation system, in which the aging degree of insulating paper is the key to evaluate the remaining life of the transformer. Currently, methanol extraction is widely used to detect the furfural content in oil to evaluate the aging state of insulating paper, but it ignores the influence of methanol produced during the operation of the transformer on the extraction results. To address this issue, this paper proposes a new method that can replace methanol extraction for detecting furfural in oil through simulation and experiment. Firstly, through simulation and experiment, it is proved that the strong vibration absorption peaks at 1677 cm-1 for furfural carbonyl and 2240 cm-1 for acetonitrile cyano can be used to establish a new method for extracting furfural content in oil using acetonitrile. A quantitative model between the concentration x of furfural in the furfural-acetonitrile mixed solution and the area y of the infrared absorption peak at 1677 cm-1 is established, with a goodness of fit of 0.9974. Secondly, a comparison between direct detection and acetonitrile extraction methods is conducted. The results show that direct detection is simple to operate, but the minimum detection concentration is 40mg/L, which is difficult to meet practical requirements. Acetonitrile extraction can reduce the minimum detection concentration to 0.1mg/L. At the same time, the extraction conditions are analyzed to determine the extraction ratio of 30, extraction times of 5, and extraction rate of 60%. Finally, the proposed detection method is applied to thermal aging tests on insulating paper of different types. The experimental results show that the proposed method has good repeatability and improves the detection resolution of furfural content. This paper combines infrared spectroscopy with acetonitrile extraction technology to open up a more efficient and practical new method for detecting furfural content in transformer oil.

Traditional fault diagnosis methods for mineral oil‐immersed power transformer based on dissolved gas analysis: Past, present and future

AbstractA key factor in ensuring the efficient and safe operation of power transformers is the early and accurate diagnosis of incipient faults. Among the tools available to achieve this goal, dissolved gas analysis (DGA) is widely used by power transformers' maintenance professionals. It is a preventive maintenance tool, used for condition monitoring, fault diagnosis and unplanned outage prevention. With the development of artificial intelligence (AI), many intelligent‐based methods using AI tools have been proposed in the literature for DGA data interpretation. Although these methods achieve high diagnostic accuracies and improve DGA efficiency, they are generally complicated and the research documented in these publications is difficult to replicate. Traditional DGA‐based methods are simple, easy to understand and implement, and widely used by power transformers' maintenance professionals. Many methods proposed in recent years overcome the limitations of the pioneer methods and are increasingly effective. The authors present a detailed and comprehensive literature review of the traditional DGA‐based methods for mineral oil‐immersed power transformer faults diagnosis. This review also addresses ways to improve the efficiency of the available traditional methods. Some pitfalls that need to be taken into account to improve the efficiency of the DGA‐based diagnostic methods are also presented.

Artificial neural network-based cooling capacity estimation of various radiator configurations for power transformers operated in ONAN mode

Power transformers submerged in oil are universally acknowledged as very useful elements in electrical power networks. A fraction of the electrical energy involved in the conversion from high to low voltages is lost as thermal energy, which is produced inside the transformer's windings and core. The effective dissipation of heat is of paramount significance and can be accomplished through the strategic installation of radiators on the tank. This study aims to examine the total cooling capacity of a radiator employed for cooling a power transformer operating in oil, natural air, and natural mode. The investigation is conducted by varying the design parameters, specifically the number of fins per radiator and the radiator length. The study also utilizes computational fluid dynamics results to achieve a substantial convergence with experimental findings. In the context of the verification study conducted for a specific study point, it was found that the simulation outcomes at a mass flow rate of 0.15 kg/s corresponded to about 7.4 % of the experimental results. Additionally, the cooling capacity value obtained was 7.2 %. In the context of machine learning, the performance of the numerical approach was assessed by employing the Bayesian regularization method. The evaluation revealed that the margin of deviation, mean squared error, and coefficient of determination (R2) metrics yielded values of −0.001 %, 1.32E-02, and 0.99930, respectively.

A study on electro-thermal-physical properties of mixed mineral oil and vegetable oil as an alternative for liquid insulation in power transformers

ABSTRACT The power transformer plays a crucial role in the power system’s network for distributing power. The efficient operation of power transformers is mainly based on the efficient liquid insulation used for cooling purposes. Mineral Oil (MO) derived from petroleum is traditionally used for insulation in power transformers. However, its lower thermal degradation and thermal conductivity may influence the performance of the transformer. The non-degradable property and low flash and fire points also affect environmental safety. The aforementioned attributes encouraged researchers to propose an alternative transformer oil for power transformers. In literature, various studies have been carried out to analyze Vegetable Oil (VO) as an alternative to petroleum-based MO; However, due to high viscosity, acidity may affect the performance of the transformer. Hence, this article proposes mixed MO with VO as an alternative for liquid insulation in power transformers. This study uses different ratios of coconut oil, palm oil and sunflower oil for investigation with MO. The experimental results show that the electrical, thermal, and physical properties of mixed transformer oil are better than those of MO and VOs. The results show that a higher proposition of VO has shown better performance. The thermal conductivity of mixed VOs with MO has increased by 6–10% compared to the MO. Both flash and fire points of mixed VO with MO increased by 50–70% compared to MO. The load has been performed to analyze the efficiency and operating temperature of the transformer; results show that the mixed VO with MO combination has 32.3% higher efficiency and 16 ∘ C lower operating temperature compared to MO. Further, the various aging characteristics of the transformer oil are also discussed in this study.

Research on Condition Assessment Method of Transformer Based on Cloud Model

The traditional evaluation method of transformer has the problems of strong subjectivity of threshold value and fuzzy correlation of state quantity, which is difficult to accurately evaluate the operation quality of transformer. In order to solve these problems, this paper proposes a comprehensive evaluation method of transformer performance evaluation and operation quality based on cloud model. The evaluation index system of power transformer operation quality considering performance is constructed, including target layer, performance layer and index layer. The triangular fuzzy number is used to optimize the set of expert opinions, and the indexes are combined and weighted by fuzzy analytic hierarchy process and entropy weight method. After the data is degraded, the membership matrix of transformer operation index is constructed by using the cloud model, and the performance state weighting and membership degree are comprehensively considered to realize the performance evaluation of transformer and the comprehensive evaluation of operation quality based on performance. By using this method to predict 10 transformers in operation for a period of one month, it is possible to accurately identify abnormal situations on site, with a prediction accuracy of 96.28%. Compared with the traditional method of “predicting first and then diagnosing”, it has improved by 14.32%, overcoming the shortcomings of subjectivity, ambiguity, and ignoring the correlation of state variables in traditional evaluation methods. This method provides new ideas and methods for accurate evaluation of transformers.

Improvement of operation and maintenance efficiency of power transformers based on knowledge graphs

AbstractCurrently, the digital transformation of the power grid is underway, and the intelligent health management technology for power transformers is rapidly advancing. However, there are issues in the operation and maintenance process, such as weak information correlation and low decision‐making efficiency. Knowledge graphs have been applied in other industrial fields, such as spacecraft maintenance, to significantly improve knowledge query efficiency. However, there is a lack of literature on knowledge graph construction in the field of power transformer operation and maintenance. Additionally, there is limited publicly available data and difficulties in effectively mining operation and maintenance knowledge in this field. A method for constructing a knowledge graph for power transformer operation and maintenance based on ALBERT is proposed. Firstly, publicly available literature in the field of power transformers is collected, and a sample enhancement method using regular matching is used to enrich the semi‐structured corpora, such as power system accident investigation reports, to construct a training dataset for power transformer operation and maintenance. Then, the ALBERT‐BiLSTM‐CRF deep learning algorithm is applied to extract power transformer operation and maintenance entities from the relevant literature and accident investigation reports, and this method is compared with traditional deep learning algorithms to demonstrate its superiority. Subsequently, the ALBERT‐BiLSTM‐Attention deep learning algorithm, which incorporates ALBERT and attention mechanism, is utilised to extract relationships between power transformer operation and maintenance entities. Compared to other deep learning algorithms, this algorithm demonstrates better performance in the domain‐specific texts of power transformer operation and maintenance. Finally, the Neo4j graph database is used to visualise and present the knowledge graph, enabling decision support based on the power transformer operation and maintenance knowledge graph.

Development of a simulation model for assessing the technical condition of oil power transformers by measuring vibroacoustic parameters

In the electric power industry, oil-type power transformers are considered the main device that ensures reliable transmission of electric power, and their technical capabilities are analyzed. Factors affecting long-term reliable operation of oil-fired power transformers are investigated. In this paper, a simulation model is developed to assess the condition of oil power transformers during rapid diagnosis. A mathematical model has also been developed that allows calculating the absorption coefficients and the general technical condition of oil power transformers depending on the value of the input variables of oil power transformers operating in city power networks. The authors developed a Simulink model for rapid assessment of the technical condition of the oil power transformer. A block diagram is created that allows to determine the value of the technical condition of the oil power transformer according to the vibroacoustic parameters.

The Influence of Outdoor Air Temperature Growth on the Operation of Power Transformers

The Influence of Outdoor Air Temperature Growth on the Operation of Power Transformers

K2CO3‐loaded γ‐Al2O3 for acid removal in waste transformer oil

AbstractTransformer oil plays a critical role in ensuring the insulation and normal operation of power transformers. However, during operation, the transformer oil would undergo oxidation and degradation, leading to the formation of acids and polar compounds that increase its acidity and degrade its performance. Hence, the recycling and regeneration of aged transformer oil is an essential practice in transformer maintenance, contributing significantly to the extension of a transformer's operational lifespan and the reduction of potential risks. Adsorption separation is a preferred method for regeneration, offering simplicity and high efficiency. This study focuses on impregnated γ‐Al2O3 as an adsorbent for acidity removal in waste transformer oil. We conducted static adsorption experiments to investigate the optimal loading amount of the active component and the best calcination temperature for acid removal. XRD technique is employed to monitor the phase changes of the carrier during the calcination process. BET analysis is used to observe variations in the specific surface area and pore volume of the adsorbent during the modification process. DTG‐TGA is utilized to assess the weight loss during the calcination process. FTIR is employed to observe changes in functional groups during modification and adsorption processes. Along with these investigations, a study is also carried out to assess the dynamic acid removal performance of the acid‐removing adsorbent loaded into a fixed bed. The findings contribute to the development of sustainable methods for regenerating transformer oil, ensuring efficient and environmentally friendly operations.

Research on Changes in the Phase Shift Angle and Admittance of the Cellulose–Bio-Oil Composite under the Influence of Increasing Moisture during the Long-Term Operation of Power Transformers

In this study, the temperature–frequency dependencies of the pressboard–bio-oil–water nanoparticle composite’s fundamental parameters—phase shift angle and admittance—were examined using the Frequency Domain Spectroscopy method. Measurements were conducted in a frequency range of 10−4 Hz to 5 × 103 Hz at temperatures ranging from 293.15 K to 343.15 K, with a step of 10 K. The temperature stabilization accuracy was less than ±0.05 K. A total of 15 Arrhenius plots were determined for various phase shift angle values, from which 15 values and the average activation energy of relaxation time were determined. Similarly, the values of the activation energy of admittance relaxation time and the activation energy of admittance were determined. It was established that all three average values are identical within the bounds of uncertainty. Based on 45 values, a generalized activation energy was determined, with a value of ΔW ≈ (1.032 ± 0.0196) eV. Using the generalized activation energy value, the phase shift angle curves determined for all temperatures were recalculated to a temperature of 293.15 K. It was found that after conversion, all curves perfectly overlap. A similar operation was carried out for the frequency dependencies of admittance. In this case, too, the recalculated dependencies perfectly match. This means that the shape of the frequency dependencies of the phase shift angle and admittance depends solely on the moisture content in the pressboard–bio-oil–water nanodrop composite. The position of the curves in doubly logarithmic coordinates depends only on temperature and is determined by the generalized activation energy Using the generalized activation energy determined in this study will allow for the development of accurate methods for estimating moisture content in cellulose insulation of power transformers containing bio-oil. This will contribute to the detection of critical moisture content, which is about 5% by weight, and to the elimination of the risk, associated with such a high moisture content, of catastrophic failure of power transformers.

Developing a novel optical sensor for condition assessment of aged mineral oil based on carbon dots

Power transformers are major linking equipment in power systems, most of which use oil as insulating medium. This work aims to develop a sensitive opto-active material for effective condition assessment of mineral oil. Thermal aging is performed in the lab under conditions that simulate the actual operation of power transformers. Novel carbon dots are prepared in the lab to be used as an optical sensor for the aged mineral oil. The interaction between a fixed 2 mL of carbon dots and mineral oil samples aged for different times and at different concentrations is studied. A fixed oil concentration of 250 µL is selected and the emission spectra are linearly correlated with aging and dielectric dissipation factor. The in-service limits of dielectric dissipation factor are used to judge the mineral oil’s condition. The obtained photoluminescence spectra proved the suitability of this novel developed carbon dots for condition assessment of mineral oil.

New Algorithm Applied to Transformers' Failures Detection Based on Karhunen–Loève Transform

Industry and science have been growing attention to developing systems that ensure the integrity of high voltage devices like power transformers. The goal is to avoid unexpected stoppages by detecting incipient failures before they become a major problem. In this context, the detection of discharge activity is an effective way to assess the condition operation of power transformers since this type of flaw can lead the transformer to total failure. The effectiveness of the fault diagnosis systems is related to their capability to distinguish the types of discharges since different flaws require different maintenance planning. This article proposes a new data analysis which combined the frequency spectrum of the signals with the Karhunen-Loève Transform to perform self-organization maps. The effectiveness of this analysis was validated by comparing it with the Fundamental Signals Properties Classification Technique, which is widely applied for pattern recognition.Two types of sensing techniques were assessed in order to enhance the capability of the new approach. Results indicated that the new methodology presented lower standard deviation for data classification, being a promising tool to monitoring systems.

Development and Implementation of Transformer Breather Health Monitoring System Using IoT

Abstract: The reliable and efficient operation of power transformers is crucial for maintaining a stable electrical grid. One critical aspect of transformer maintenance is monitoring the health of the transformer breather, which plays a vital role in preventing the ingress of moisture and contaminants. This paper proposes an innovative approach for health monitoring of transformer breathers using the Internet of Things (IoT). By integrating IoT technologies with transformer breather systems, real-time monitoring and analysis of breather conditions can be achieved, enabling proactive maintenance and optimizing the lifespan of the transformer. The proposed IoT-based health monitoring system consists of interconnected breather units equipped with sensors to capture relevant data such as humidity levels, temperature, color of the silica gel, and gas concentrations. These sensors continuously gather data and transmit it to a central monitoring system via wireless communication protocols. The IoT-based health monitoring of transformer breathers offers several advantages over conventional methods. It enables continuous, real-time monitoring, eliminating the need for manual inspections and reducing the risk of unexpected failures.

Improving IEC thermal model for oil natural air natural transformers using optimised parameters based on dynamic simulation

AbstractAccurate assessment of hot‐spot temperature is essential for the safe operation of power transformers. Existing dynamic thermal models cannot estimate hot‐spot temperature accurately since some input parameters are roughly determined by transformer capacity and cooling mode while ignoring the effect of winding structure, tank dimensions, and material physical properties. To improve the accuracy of temperature assessment, empirical parameters of the IEC thermal model including thermal constants, winding and oil exponents are optimised with the help of numerical simulation in this article. Based on energy conservation and heat transfer theory, a computational fluid dynamic (CFD) model of a transformer in oil natural air natural (ONAN) cooling mode is established. This CFD model simulates the entity's structure, sizes, and multi‐stage heat dissipation processes realistically, so it can more precisely calculate the dynamic hot‐spot temperature. According to the simulated temperature curves at different operating conditions, the thermal constants and oil exponent are estimated using non‐linear regression, and the winding exponent is optimised using linear regression. A case study is conducted on an ONAN transformer. It shows the improved IEC model with optimised parameters can more accurately evaluate hot‐spot temperature, and the absolute error is decreased by 2.4 K (38.7%) compared with traditional thermal models.

The diffusion characteristics of POSS under electric field and non-uniform temperature field in natural ester insulating oil

The natural ester insulating oil is a green and environmentally friendly liquid dielectric, and nano additives can effectively improve its dielectric and heat transfer performance. However, nano additives are prone to agglomeration, especially under the influence of electric and temperature fields, which can impact the prolonged stable operation of power transformers. In this paper, the diffusion properties of caged Polysilsesquioxane (POSS) molecules in natural ester insulating oil under external electric field and non-uniform temperature field were investigated via simulation, and the effects of temperature and electric field on POSS molecules were expounded from the perspective of interaction. The results show that the high temperature difference and strong electric field can effectively improve the mobility of POSS molecules, and the temperature difference has a greater impact on the diffusion. The decrease of interaction energy is mainly due to the negative positive interaction between Si-O molecules in POSS molecules and insulating oil at high temperature, which counteracts the attraction of branch chains. At the same time, POSS molecules in the electric field will also show obvious polarization due to temperature changes, which further reduces the interaction between POSS molecules and insulating oil molecules. In addition, the electrostatic interaction energy decreases most obviously, which is the main reason that affects the migration characteristics of POSS molecules in natural ester insulating oil. This study provides a theoretical explanation for the migration mechanism of insulating oil in the microenvironment.

Variable Reactance Criteria to Mitigate Voltage Deviations in Power Transformers in Light- and Over-Load Conditions

In this paper, variable reactance (VR) criteria are proposed to mitigate voltage deviations in power transformers under light-load inductive and capacitive conditions, as well as for over-load conditions. Under capacitive load conditions, power transformers are affected by the Ferranti effect as much as AC lines are and can suffer damage if a large over-voltage is present at the secondary winding. A classical solution for this is the installation of expensive and bulky inductive reactors at different locations of the AC lines to absorb the reactive power. Instead, this paper addresses VR techniques focused on power transformer reactance modification to compensate for the over-voltage. With these techniques, the Ferranti effect on power lines can also be reduced. Another benefit is the cancellation of over-voltages whose cause is different from the Ferranti effect, namely under inductive load conditions. In addition, they can also enhance the parallel operation of power transformers by allowing more flexibility for overload sharing among transformers. The VR techniques are derived from the Kapp phasor-diagram theory and have been validated experimentally at a small scale in the laboratory. When implemented in a big network, they can also improve the load-flow voltage and AC line-loading profiles and even increase the power factor of certain generators.

Analysis on Acoustic Disturbance Signals Expected During Partial Discharge Measurements in Power Transformers

This paper presents comparative analysis of various acoustic signals expected during partial discharge (PD) measurements in operating power transformer. Main purpose of the paper is to yield relevant and reliable method to distinguish between various acoustic emission (AE) signals emitted by PD and other sources, with particular consideration of real-life results rather than laboratory simulations. Therefore, selected examples of real-life AE signals registered in seven different power transformers, under normal operation conditions, within few years are showed and analyzed. Five scenarios are investigated, which represent five types of AE sources: PD generated by artificial sources, and next four real-life sources (including PD in working transformer, oil flow, oil pumps and core). Several different signal processing methods are applied and compared in order to identify the PD signals. As a result, an energy patterns analysis based on the wavelet decomposition is found as the most reliable tool for identification of PD signals. The presented results may significantly support the process of interpretation of the PD measurement results, and may be used by field engineers as well as other researchers involved in PD analysis using AE method. Finally, observed properties also provide a solid basis for establishing or improving complete classification method based on the artificial intelligence algorithms.